Integrative Multi-omics Analysis to Predict Monoclonal Gammopathies Clinical Evolution
Integrative Multi-omics Analysis to Predict Monoclonal Gammopathies Clinical Evolution
This prospective, multicenter, observational study aims to identify molecular and immunological markers associated with disease progression in patients with monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). By integrating genomic, transcriptomic, immunophenotypic, and oral microbiome analyses, the study seeks to characterize the biological mechanisms underlying the transition to symptomatic multiple myeloma (MM). The study also includes in vitro modeling to investigate bone damage and immune dysfunction. Healthy volunteers (HV) undergoing joint replacement surgery for osteoarthritis will serve as controls. The ultimate goal is to improve early risk stratification and support future preventive strategies through a multi-omics approach. There is a pressing need for new strategies to identify high-risk individuals based on biological rather than purely clinical parameters. This study proposes an integrative, multi-omics approach to investigate the transition from MGUS/SMM to MM. By analyzing the immunome and oral microbiome alongside molecular profiling, the goal is to identify reliable biomarkers of progression. The resulting insights could be enable more accurate risk stratification and guide the design of future preventive clinical trials aimed at delaying or halting disease evolution.
Multiple myeloma (MM) is a hematologic malignancy characterized by the clonal proliferation of antibody-secreting plasma cells in the bone marrow. It accounts for approximately 10% of all blood cancers, with an incidence of 3-5per 100.000 individuals in Westen countries. MM is an incurable disease that leads to severe bone destruction and fractures due to the abnormal interaction between malignant plasma cells and the bone marrow microenvironment. Although new therapies have improved survival, MM remains a complex and genetically heterogeneous disease. Genomic instability is a hallmark of MM and includes both chromosomal abnormalities and gene mutations. Tumors may presenta s hyperdiploid - with multiple trisomies - or non-hyperdiploid, often involving translocations at the immunoglobulin heavy chain locus (IGH). These genetic differences impact prognosis. Additional recurrent alterations, such as deletions (13q, 17q), gains (1q), and mutations in genes like KRAS, NRAS, TP53, and BRAF, further illustrate the disease's biological diversity. Molecular profiling techniques, such as RNA sequencing and gene expression arrays, have identified gene expression patterns that correlate with prognosis, though only a few are currently used in clinical practice. MM is consistently preceded by two asymptomatic precursor conditions: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). These conditions are prevalent in older adults and share many molecular features with symptomatic MM, yet only a small fraction of cases progress annually - about 1% for MGUS and 10% for SMM. Disease evolution appears to depend not only on intrinsic genetic changes but also on interactions with the bone marrow microenvironment, which includes stromal cells, dendritic cells, T cells (especially Th17), NK cells and myeloid-derived suppressors cells. Immune dysfunction, antigen presentation defects, expansion of immunosuppressive cells, and high levels of inhibitory cytokines contribute to the emergence of an immunosuppressive niche that enables myeloma cells to escape immune surveillance and progress. Immunomodulatory drugs (ImiDs) and monoclonal antibodies, which can reactivate immune responses, are therefore central to treatment strategies. Recent evidence also suggests a link between the microbiota and disease progression. In experimental models, alterations in gut microbiota have been shown to affect immune responses, influencing disease onset. Currently available prognostic tools mainly reflect tumor burden rather than underlying biology. As such, they fail to accurately predict disease progression.
Inclusion Criteria:
HEALTHY VOLUNTEERS (HV)
Exclusion Criteria:
Patients:
Healthy Volunteers:
noemi.puccio@ausl.re.it
Catania, Italy
Palermo, Italy
cecilia.fabiano@ausl.re.it
barbara.gamberi@ausl.re.it